> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Evaluate Models
> Evaluate one or several models
In Adaptive Engine, you can evaluate models to know which off-the-shelf model performs best on your task, or how much did your fine-tuned model improve vs. its base model or others after training.
## Launch an evaluation
Evaluation is done by [launching a run](/v0.7/recipes/runs) of an evaluation [recipe](/v0.7/recipes/recipes). An Evaluation recipe is a recipe that produces an `EvaluationArtefact`.
Adaptive provides a [built-in recipe](/v0.7/recipes/recipes#built-in-recipes) for most evaluation use-cases. For more tailored usage (using custom graders for example), you can create your own recipe with your own completion grading strategy by following our guides [Custom Graders](/v0.7/recipes/guides/graders) and [Write an Evaluation Recipe](/v0.7/recipes/guides/eval)
## Visualize results
Once an Evaluation run is finished, it will produce an Evaluation Artifact. This will contain:
1. An Evaluation score table that summarises all model's scores for the graders that were used during the eval
2. A detailed list of [interactions](/v0.7/interactions/overview) from all graded samples in the evaluated dataset.
Evaluation table}>
